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Calculate empirical type 2 response probabilities

Source: R/type2_draws.R
type2_probabilities.Rd

Given a dataset data, determine the probability of each type 2 response, optionally conditional on stimulus and/or type 1 response.

Usage

type2_probabilities(
  data,
  ...,
  .stimulus = "stimulus",
  .response = "response",
  .confidence = "confidence",
  .joint_response = "joint_response",
  K = NULL,
  by_stimulus = TRUE,
  by_response = TRUE,
  by_correct = FALSE
)

Arguments

data

The data frame to aggregate

...

Grouping columns in data. These columns will be converted to factors.

.stimulus

The name of "stimulus" column

.response

The name of "response" column

.confidence

The name of "confidence" column

.joint_response

The name of "joint_response" column

K

The number of confidence levels in data. If NULL, this is estimated from data using the maximum value of either the confidence column or joint response column.

by_stimulus

If TRUE (default), calculate type 2 response probabilities conditional on stimulus.

by_response

If TRUE (default), calculate type 2 response probabilities conditional on type 1 response.

by_correct

If FALSE (default), calculate type 2 response probabilities conditional on stimulus and/or type 1 response. If TRUE, instead calculate probabilities conditional on accuracy.

Value

A tibble with columns:

  • ...: the grouping columns in data

  • {.stimulus} (if by_stimulus=TRUE): the stimulus

  • {.response} (if by_response=TRUE): the type 1 response

  • correct: the accuracy (if by_correct=TRUE)

  • {.confidence}: the type 2 response

  • {.joint_response} (if by_response=TRUE): the joint type 1/type 2 response

  • n: the number of rows in data with the corresponding stimulus (if by_stimulus=TRUE), response (if by_response=TRUE), correct (if by_correct=TRUE) and confidence

  • p: the proportion of rows in data with the corresponding response (per stimulus if by_stimulus=TRUE and per response if by_response=TRUE)

See also

type2_draws()

Examples

# calculate type 2 response probabilities by stimulus
type2_probabilities(example_data())
#> `hmetad` has inferred that there are K=4 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
#> # A tibble: 16 × 6
#> # Groups:   stimulus, response [4]
#>    stimulus response confidence joint_response     n      p
#>       <int>    <int>      <int>          <int> <int>  <dbl>
#>  1        0        0          1              4    86 0.244 
#>  2        0        0          2              3   101 0.287 
#>  3        0        0          3              2    94 0.267 
#>  4        0        0          4              1    71 0.202 
#>  5        0        1          1              5    74 0.5   
#>  6        0        1          2              6    44 0.297 
#>  7        0        1          3              7    24 0.162 
#>  8        0        1          4              8     6 0.0405
#>  9        1        0          1              4    75 0.478 
#> 10        1        0          2              3    46 0.293 
#> 11        1        0          3              2    26 0.166 
#> 12        1        0          4              1    10 0.0637
#> 13        1        1          1              5    86 0.251 
#> 14        1        1          2              6   104 0.303 
#> 15        1        1          3              7    75 0.219 
#> 16        1        1          4              8    78 0.227 

# calculate type 2 response probabilities by condition, averaging over stimuli
type2_probabilities(sim_metad_condition(), condition, by_stimulus = FALSE)
#> `hmetad` has inferred that there are K=4 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
#> # A tibble: 16 × 6
#> # Groups:   condition, response [4]
#>    condition response confidence joint_response     n     p
#>        <int>    <int>      <int>          <int> <int> <dbl>
#>  1         1        0          1              4    15 0.288
#>  2         1        0          2              3    17 0.327
#>  3         1        0          3              2     8 0.154
#>  4         1        0          4              1    12 0.231
#>  5         1        1          1              5    18 0.375
#>  6         1        1          2              6    12 0.25 
#>  7         1        1          3              7     6 0.125
#>  8         1        1          4              8    12 0.25 
#>  9         2        0          1              4    15 0.349
#> 10         2        0          2              3    15 0.349
#> 11         2        0          3              2     6 0.140
#> 12         2        0          4              1     7 0.163
#> 13         2        1          1              5    19 0.333
#> 14         2        1          2              6    11 0.193
#> 15         2        1          3              7    15 0.263
#> 16         2        1          4              8    12 0.211